Technical Field
The present invention relates generally to learning technologies and, in particular, to interactive learning.
Description of the Related Art
The ability for agents to learn and adapt to their environment in a multi-agent machine learning system is a fundamental capability of such a system. It is assumed that the agents are tailored to a particular, repeatable event (e.g., the agents operate in the context of a meeting, a chat session, and so forth) and wish to act in some optimal fashion. Given what the agents are able to view of the event as it evolves, agents are tasked with predicting an optimal course of action.
Rather than reacting ab initio each time the event is repeated, the agents would benefit from building up a reservoir of experience from which they are capable of learning. Machine learning is an esoteric and difficult to use technology so that independently building separate agents that implement learning systems in their own way is especially cumbersome. For analogous reasons, creating a shared learning environment has remained elusive. What is needed is an interactive learning capability such that one could create and train a generic, non-task-specific, learning system for a given user, or set of users, that could then be accessed (and contributed to) by other, more task-specific, agents, systems or applications.